Abstract
Operation of environmentally responsive building components requires rapid prediction of the optimal adaptation of geometric shapes and positions, and such responsive configuration needs to be identified during the design process as early as possible. However, building simulation practices to characterize optimized shapes of various geometric design candidates are limited by complex simulation procedures, slow optimization, and lack of site information. This study suggests a practical approach to the design of responsive building façades by integrating on-site sensors, building performance simulation (BPS), machine-learning, and 3D geometry modeling on a unified design interface. To this end, a novel and efficient hybrid optimization algorithm, tabu-based adaptive pattern search simulated annealing (T-APSSA), was developed and integrated with wireless sensor data communication (using nRF24L01 and ESP8266 WiFi modules) on a parametric visual programming language (VPL) interface Rhino Grasshopper (0.9.0076, McNeel, Seattle, USA). The effectiveness of T-APSSA for early-stage BPS and optimal design is compared with other metaheuristic algorithms, and the proposed framework is validated by experimental optimal envelope (window shading) designs for single (daylight) and multiple (daylight and energy) objectives. Test results demonstrate the improved efficiency of T-APSSA in calculations (two to four times faster than other algorithms). This T-APSSA-integrated sensor-enabled design optimization practice supports rapid BPS and digital prototyping of responsive building façade design.
Highlights
Many in the discipline of architecture have long envisioned buildings that, against their inherent immobility, interactively respond to ambient contexts [1,2,3]
Despite many conceptual projects and methodological proposals regarding building responsiveness [6,7,9,19], the problem of how to incorporate environmental simulations of the motions of building parts is addressed less in the context of early design processes. This is mainly because (1) the existing tools are technically insufficient to support the complex design of dynamic building motion [12]; (2) it is difficult to develop a mathematical simulation algorithm that is generally applicable to the diagnosis of responsive building performance [11]; and (3) there is a lack of a standardized procedure or protocol regarding the automation and optimization of adaptively moving geometry [5,9]
This algorithm constructs a loop of SA-based iterations, and at each step candidates are selected based on the PS strategy that subdivides Ω into a k-dimensional set of mesh points
Summary
Many in the discipline of architecture have long envisioned buildings that, against their inherent immobility, interactively respond to ambient contexts [1,2,3]. Heightened interests in movable types of buildings closely pertain to a growing need for an architectural responsibility for environmental sustainability. Through dynamic settings and bioclimatic adaptations of geometrical shapes in natural and human. 2 of 28 2 of 28 enmviirtoignamtienngtse, naerregyvaclounesdumfoprtiomnpraonvdincgaribnodnooermtihsseiromnsa,l wcoitmhofourtt aanndadodpietiroantiaolnuasleeffiofcifeuneclyorbythe miitnigstaatlilnagtioenneorfgeyqucoipnmsuemntp[t1i0o,n11a].nd carbon emissions, without an additional use of fuel or the installaTtionmoafkeeqrueisppmoennsitv[e10b,u11il]d. Ignificantly delay the process of obtaining design outcomes [14,15]. FiguFrieg1u.reT1e.stTescsht secmheemofe tohfethseensseinnsgin-tgri-gtrgigergeedreedaerlayr-lsyt-asgtaegreersepsopnosnivsievefafçaaçdaededdeseisgignnoopptitmimizizaatitoionn:: Use of Wi-FiUasnedoRf aWdii-oF-ifraenqdueRnacdyioid-fernetqiufiecnatciyonid(eRnFtIiDfic)aftoior nth(eRdFeIDsi)gfnowr tohrekdspeasicgen. FiguFrieg1u.reT1e.stTescsht secmheemofe tohfethseensseinnsgin-tgri-gtrgigergeedreedaerlayr-lsyt-asgtaegreersepsopnosnivsievefafçaaçdaededdeseisgignnoopptitmimizizaatitoionn:: Use of Wi-FiUasnedoRf aWdii-oF-ifraenqdueRnacdyioid-fernetqiufiecnatciyonid(eRnFtIiDfic)aftoior nth(eRdFeIDsi)gfnowr tohrekdspeasicgen. workspace
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